GDAtools (version 1.6)

csMCA: Performs a 'class specific' MCA

Description

Performs a 'class specific' Multiple Correspondence Analysis, i.e. a variant of MCA consisting in analyzing a subcloud of individuals.

Usage

csMCA(data, subcloud = rep(TRUE, times = nrow(data)), excl = NULL, ncp = 5, 
row.w = rep(1, times = nrow(data)))

Arguments

data

data frame with n rows (individuals) and p columns (categorical variables)

subcloud

a vector of logical values and length n. The subcloud of individuals analyzed with 'class specific' MCA is made of the individuals with value TRUE.

excl

numeric vector indicating the indexes of the 'junk' categories (default is NULL). See getindexcat to identify these indexes.

ncp

number of dimensions kept in the results (default is 5)

row.w

an optional numeric vector of row weights (by default, a vector of 1 for uniform row weights)

Value

Returns an object of class 'csMCA', i.e. a list including:

eig

a list of vectors containing all the eigenvalues, the percentage of variance, the cumulative percentage of variance, the modified rates and the cumulative modified rates

call

a list with informations about input data

ind

a list of matrices containing the results for the individuals (coordinates, contributions)

var

a list of matrices containing all the results for the categories and variables (weights, coordinates, square cosine, categories contributions to axes and cloud, test values (v.test), square correlation ratio (eta2), variable contributions to axes and cloud

Details

This variant of MCA is used to study a subset of individuals with reference to the whole set of individuals, i.e. to determine the specific features of the subset. It consists in proceeding to the search of the principal axes of the subcloud associated with the subset of individuals (see Le Roux and Rouanet, 2004 and 2010).

References

Le Roux B. and Rouanet H., Multiple Correspondence Analysis, SAGE, Series: Quantitative Applications in the Social Sciences, Volume 163, CA:Thousand Oaks (2010).

Le Roux B. and Rouanet H., Geometric Data Analysis: From Correspondence Analysis to Stuctured Data Analysis, Kluwer Academic Publishers, Dordrecht (June 2004).

See Also

getindexcat, plot.csMCA, varsup, contrib, modif.rate, dimdesc.MCA, speMCA, MCA

Examples

# NOT RUN {
## Performs a 'class specific' MCA on 'Music' example data set
## ignoring every 'NA' (i.e. 'not available') categories,
## and focusing on the subset of women.
data(Music)
female <- Music$Gender=='Women'
mca <- csMCA(Music[,1:5],subcloud=female,excl=c(3,6,9,12,15))
plot(mca)
# }